Differential H2O2 Metabolism among Glioblastoma Subtypes Confers Variable Responses to Pharmacological Ascorbate Therapy Combined with Chemoradiation.

Glioblastoma (GBM), a highly lethal and aggressive central nervous system malignancy, presents a critical need for targeted therapeutic approaches to improve patient outcomes in conjunction with standard-of-care (SOC) treatment. Molecular subtyping based on genetic profiles and metabolic characteristics has advanced our understanding of GBM to better predict its evolution, mechanisms, and treatment regimens. Pharmacological ascorbate (P-AscH-) has emerged as a promising supplementary cancer therapy, leveraging its pro-oxidant properties to selectively kill malignant cells when combined with SOC. Given the clinical challenges posed by the heterogeneity and resistance of various GBM subtypes to conventional SOC, our study assessed the response of classical, mesenchymal, and proneural GBM to P-AscH-. P-AscH- (20 pmol/cell) combined with SOC (5 µM temozolomide and 4 Gy of radiation) enhanced clonogenic cell killing in classical and mesenchymal GBM subtypes, with limited effects in the proneural subtype. Similarly, following exposure to P-AscH- (20 pmol/cell), single-strand DNA damage significantly increased in classical and mesenchymal but not proneural GBM. Moreover, proneural GBM exhibited increased hydrogen peroxide removal rates, along with increased catalase and glutathione peroxidase activities compared to mesenchymal and classical GBM, demonstrating an altered H2O2 metabolism that potentially drives differential P-AscH- toxicity. Taken together, these data suggest that P-AscH- may hold promise as an approach to improve SOC responsiveness in mesenchymal GBMs that are known for their resistance to SOC.

Main Manuscript for Cell migration simulator-based biomarkers for glioblastoma.

Glioblastoma is the most aggressive malignant brain tumor with poor survival due to its invasive nature driven by cell migration, with unclear linkage to transcriptomic information. Here, we applied a physics-based motor-clutch model, a cell migration simulator (CMS), to parameterize the migration of glioblastoma cells and define physical biomarkers on a patient-by-patient basis. We reduced the 11-dimensional parameter space of the CMS into 3D to identify three principal physical parameters that govern cell migration: motor number - describing myosin II activity, clutch number - describing adhesion level, and F-actin polymerization rate. Experimentally, we found that glioblastoma patient-derived (xenograft) (PD(X)) cell lines across mesenchymal (MES), proneural (PN), classical (CL) subtypes and two institutions (N=13 patients) had optimal motility and traction force on stiffnesses around 9.3kPa, with otherwise heterogeneous and uncorrelated motility, traction, and F-actin flow. By contrast, with the CMS parameterization, we found glioblastoma cells consistently had balanced motor/clutch ratios to enable effective migration, and that MES cells had higher actin polymerization rates resulting in higher motility. The CMS also predicted differential sensitivity to cytoskeletal drugs between patients. Finally, we identified 11 genes that correlated with the physical parameters, suggesting that transcriptomic data alone could potentially predict the mechanics and speed of glioblastoma cell migration. Overall, we describe a general physics-based framework for parameterizing individual glioblastoma patients and connecting to clinical transcriptomic data, that can potentially be used to develop patient-specific anti-migratory therapeutic strategies generally.

Glioblastoma genetic drivers dictate the function of tumor-associated macrophages/microglia and responses to CSF1R inhibition.

BACKGROUND: Tumor-associated macrophages/microglia (TAMs) are prominent microenvironment components in human glioblastoma (GBM) that are potential targets for anti-tumor therapy. However, TAM depletion by CSF1R inhibition showed mixed results in clinical trials. We hypothesized that GBM subtype-specific tumor microenvironment (TME) conveys distinct sensitivities to TAM targeting. METHODS: We generated syngeneic PDGFB- and RAS-driven GBM models that resemble proneural-like and mesenchymal-like gliomas, and determined the effect of TAM targeting by CSF1R inhibitor PLX3397 on glioma growth. We also investigated the co-targeting of TAMs and angiogenesis on PLX3397-resistant RAS-driven GBM. Using single-cell transcriptomic profiling, we further explored differences in TME cellular compositions and functions in PDGFB- and RAS-driven gliomas. RESULTS: We found that growth of PDGFB-driven tumors was markedly inhibited by PLX3397. In contrast, depletion of TAMs at the early phase accelerated RAS-driven tumor growth and had no effects on other proneural and mesenchymal GBM models. In addition, PLX3397-resistant RAS-driven tumors did not respond to PI3K signaling inhibition. Single-cell transcriptomic profiling revealed that PDGFB-driven gliomas induced expansion and activation of pro-tumor microglia, whereas TAMs in mesenchymal RAS-driven GBM were enriched in pro-inflammatory and angiogenic signaling. Co-targeting of TAMs and angiogenesis decreased cell proliferation and changed the morphology of RAS-driven gliomas. CONCLUSIONS: Our work identifies functionally distinct TAM subpopulations in the growth of different glioma subtypes. Notably, we uncover a potential responsiveness of resistant mesenchymal-like gliomas to combined anti-angiogenic therapy and CSF1R inhibition. These data highlight the importance of characterization of the microenvironment landscape in order to optimally stratify patients for TAM-targeted therapy.

Identification of five important genes to predict glioblastoma subtypes.

BACKGROUND: Glioblastoma (GBM), the most common and aggressive primary brain tumour in adults, has been classified into three subtypes: classical, mesenchymal, and proneural. While the original classification relied on an 840 gene-set, further clarification on true GBM subtypes uses a 150-gene signature to accurately classify GBM into the three subtypes. We hypothesized whether a machine learning approach could be used to identify a smaller gene-set to accurately predict GBM subtype. METHODS: Using a supervised machine learning approach, extreme gradient boosting (XGBoost), we developed a classifier to predict the three subtypes of glioblastoma (GBM): classical, mesenchymal, and proneural. We tested the classifier on in-house GBM tissue, cell lines, and xenograft samples to predict their subtype. RESULTS: We identified the five most important genes for characterizing the three subtypes based on genes that often exhibited high Importance Scores in our XGBoost analyses. On average, this approach achieved 80.12% accuracy in predicting these three subtypes of GBM. Furthermore, we applied our five-gene classifier to successfully predict the subtype of GBM samples at our centre. CONCLUSION: Our 5-gene set classifier is the smallest classifier to date that can predict GBM subtypes with high accuracy, which could facilitate the future development of a five-gene subtype diagnostic biomarker for routine assays in GBM samples.

An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma.

Diverse genetic, epigenetic, and developmental programs drive glioblastoma, an incurable and poorly understood tumor, but their precise characterization remains challenging. Here, we use an integrative approach spanning single-cell RNA-sequencing of 28 tumors, bulk genetic and expression analysis of 401 specimens from the The Cancer Genome Atlas (TCGA), functional approaches, and single-cell lineage tracing to derive a unified model of cellular states and genetic diversity in glioblastoma. We find that malignant cells in glioblastoma exist in four main cellular states that recapitulate distinct neural cell types, are influenced by the tumor microenvironment, and exhibit plasticity. The relative frequency of cells in each state varies between glioblastoma samples and is influenced by copy number amplifications of the CDK4, EGFR, and PDGFRA loci and by mutations in the NF1 locus, which each favor a defined state. Our work provides a blueprint for glioblastoma, integrating the malignant cell programs, their plasticity, and their modulation by genetic drivers.

REST-DRD2 mechanism impacts glioblastoma stem cell-mediated tumorigenesis.

BACKGROUND: Glioblastoma (GBM) is a lethal, heterogeneous human brain tumor, with regulatory mechanisms that have yet to be fully characterized. Previous studies have indicated that the transcriptional repressor REST (repressor element-1 silencing transcription factor) regulates the oncogenic potential of GBM stem cells (GSCs) based on level of expression. However, how REST performs its regulatory role is not well understood. METHODS: We examined 2 independent high REST (HR) GSC lines using genome-wide assays, biochemical validations, gene knockdown analysis, and mouse tumor models. We analyzed in-house patient tumors and patient data present in The Cancer Genome Atlas (TCGA). RESULTS: Genome-wide transcriptome and DNA-binding analyses suggested the dopamine receptor D2 (DRD2) gene, a dominant regulator of neurotransmitter signaling, as a direct target of REST. Biochemical analyses and mouse intracranial tumor models using knockdown of REST and double knockdown of REST and DRD2 validated this target and suggested that DRD2 is a downstream target of REST regulating tumorigenesis, at least in part, through controlling invasion and apoptosis. Further, TCGA GBM data support the presence of the REST-DRD2 axis and reveal that high REST/low DRD2 (HRLD) and low REST/high DRD2 (LRHD) tumors are specific subtypes, are molecularly different from the known GBM subtypes, and represent functional groups with distinctive patterns of enrichment of gene sets and biological pathways. The inverse HRLD/LRHD expression pattern is also seen in in-house GBM tumors. CONCLUSIONS: These findings suggest that REST regulates neurotransmitter signaling pathways through DRD2 in HR-GSCs to impact tumorigenesis. They further suggest that the REST-DRD2 mechanism forms distinct subtypes of GBM.

IGFBP2 promotes neural stem cell maintenance and proliferation differentially associated with glioblastoma subtypes.

Neural stem cells (NSCs) give rise to the central nervous system (CNS) and persist in certain areas of adult brains for replenishing damaged differentiated cells. The loss of the balance between NSC self-renewal and differentiation could lead to tumor formation such as the occurrence of glioblastoma (GBM), the most common and deadly human brain tumor, which could be derived from neural stem or stem-like cells. Early studies showed that insulin-like growth factor binding protein 2 (IGFBP2) mRNA levels were maintained high during the fetal brain development but decreased in the adult brains. We previously reported that IGFBP2 was frequently overexpressed in GBMs, which was correlated with GBM recurrence and poor survival and promoted glioma progression. However, the role of IGFBP2 in the CNS was not investigated yet, whose understanding will help elucidate IGFBP2 functions in GBM. In the study, we identify IGFBP2 as a critical molecule for mouse NSC maintenance. IGFBP2 is highly expressed in NSCs, and its expression exhibits an apical-basal pattern in the neural tube with a higher apical level and decreased with NSC differentiation during the CNS development. IGFBP2 promotes NSC self-renewal and proliferation but inhibits its differentiation to neurons and astrocytes. The knockdown of IGFBP2 significantly affected the expression of cell cycle, Notch pathway, and neural stemness and differentiation genes in NSCs. Further, the expression of IGFBP2-regulated cell cycle genes is significantly correlated with IGFBP2 expression in non-Mesenchymal GBM subtypes including Classical, Proneural, and Neural subtypes and of its Notch pathway genes differentially associated in the four GBM subtypes, altogether suggesting its critical and similar functions in NSCs and GBM cells.

Heterogenic expression of stem cell markers in patient-derived glioblastoma spheroid cultures exposed to long-term hypoxia.

AIM: To investigate the time profile of hypoxia and stem cell markers in glioblastoma spheroids of known molecular subtype. MATERIALS & METHODS: Patient-derived glioblastoma spheroids were cultured up to 7 days in either 2% or 21% oxygen. Levels of proliferation (Ki-67), hypoxia (HIF-1α, CA9 and VEGF) and stem cell markers (CD133, nestin and musashi-1) were investigated by immunohistochemistry. RESULTS: Hypoxia markers as well as CD133 and partially nestin increased in long-term hypoxia. The proliferation rate and spheroid size were highest in normoxia. CONCLUSION: We found differences in hypoxia and stem cell marker profiles between the patient-derived glioblastoma cultures. This heterogeneity should be taken into consideration in development of future therapeutic strategies.

Heterogeneous phenotype of human glioblastoma: in vitro study.

Glioblastomas (GBMs) are the most lethal primary brain tumours. Increasing evidence shows that brain tumours contain the population of stem cells, so-called cancer stem cells (CSCs). Stem cell marker CD133 was reported to identify CSC population in GBM. Further studies have indicated that CD133 negative cells exhibiting similar properties and are able to initiate the tumour, self-renew and undergo multilineage differentiation. GBM is a highly heterogeneous tumour and may contain different stem cell populations with different functional properties. We characterized five GBM cell lines, established from surgical samples, according to the marker expression, proliferation and differentiation potential. CD133 positive cell lines showed increased proliferation rate in neurosphere condition and marked differentiation potential towards neuronal lineages. Whereas two cell lines low-expressing CD133 marker showed mesenchymal properties in vitro, that is high proliferation rate in serum condition and differentiation in mesenchymal cell types. Further, we compared therapy resistance capacity of GBM cell lines treated with hydroxyurea. Our results suggest that CSC concept is more complex than it was believed before, and CD133 could not define entire stem cell population within GBM. At least two different subtypes of GBM CSCs exist, which may have different biological characteristics and imply different therapeutic strategies.